1. Field of the Invention
The present invention relates to a method of processing digital image data which represents a physical image. More particularly, the invention relates to a method of interpolating the digital image data. A circuit for implementing the novel method is also disclosed.
Korean Patent Application No. 93-419 is incorporated by reference for all purposes.
2. Description of the Related Art
The conventional technique for interpolating digital image data which represents a physical image uses a method based on a nearest-neighbor interpolation (NNI). This NNI method, regardless of the edge information and the correlation between surrounding pixels, directly interpolates the digital image data by the value of the nearest neighboring pixel. Then, a movement filter is repeatedly used for the image which has been interpolated by the NNI method to produce a family interpolated and expanded image. It should be noted the NNI method does not greatly influence the quality of a picture with a low magnification factor but produces large, rectangular blocks in highly magnified images. Moreover, this blocking, or mosaic phenomena, produced in the later case, can be removed by means of a movement averaging filter. For higher magnifications, however, a greater number of repetitions of the movement-averaging filtering becomes necessary and, if these repetitions are too numerous, the smoothness is over-emphasized, which causes a distinct degradation in the contrast of the image. This is because the conventional technique exhibits a diagonal smoothness which is poorer than that of the horizontal or vertical directions. The manner in which the diagonal interpolation is affected is described below, in terms of the conventional image interpolation technique 01.
FIG. 1 shows the supporting region of the zero-order interpolated image of the conventional technique, in the case of a magnification factor of two. Here, x(i,j) denotes the strength of the (i,j)th pixel of the image and is movement-averaged in the horizontal direction, which results in equations (1a) through (1d). EQU y.sub.h (i,j)=1/2{x(i,j)+x(i+1,j)} (1a) EQU y.sub.h (i+1,j)=1/2{x(i+1,j)+x(i+2,j)} (1b) EQU y.sub.h (i,j+1)=1/2{x(i,j+1)+(i+1,j+1)} (1c) EQU y.sub.h (i+1,j+1)=1/2{x(i+1,j+1)+x(i+2,j+1)} (1d)
wherein y.sub.h (i,j) denotes the magnitude of x components after movement-averaging in the horizontal direction.
Next, the x components are again movement-averaged in the vertical direction, using the above equations and thereby obtaining equations (2a) through (2d). ##EQU1##
It should be noted that, in the above equations, only one common component x(i+1,j+1) exists between the pixel y(i,j) and the diagonally neighboring pixel y(i+1,j+1). On the other hand, two common components exist between horizontally or vertically neighboring pixels. The difference in the number of common components in different directions (i.e., the horizontal/vertical direction versus diagonally) does not satisfy the symmetry of direction in two-dimensional image data. This directional asymmetry causes the mosaic phenomenon.
To explain why the mosaic phenomenon occurs, simple image data can be taken as an example. FIG. 2 shows image data containing the edge of an image in the diagonal direction. Here, it is assumed that the magnitude of pixel a is much greater than that of pixel b. FIG. 3 shows the result produced by twice-zero-order interpolating the image encompassed by the dotted lines of FIG. 2, using the NNI method.
FIG. 4 shows the result of movement-averaging the image of FIG. 3, in the horizontal/vertical direction. In FIG. 4, the values of y(i+2,j), y(i+1,j+1) and y(i.j+2) become 4b, a+3b and 4b, respectively. Therefore, when a is much greater than b, a+3b becomes much greater than 4b, so that the diagonally formed edge collapses. Moreover, the slope of the edge is changed by the collapsed the edge and, thus, the mosaic phenomenon occurs.